DETAILED ACTION
This Office Action is in response to the amendment filed on February 05, 2026. Claims 1 - 6 are presented for examination. Claims 1 - 6 are rejected and this Office Action is being made Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on February 05, 2026 has been entered and considered by the examiner. Based on the amendment to overcome the 101 rejections, and the amendment to overcome the 112 rejections, the rejections under 35 U.S.C. 101 and 112 are withdrawn.
Response to Arguments
Applicant's arguments filed February 05, 2026 have been fully considered but they are not persuasive.
With regards to the objection to the drawings, the applicants argue the Replacement Sheet for FIG. 4 improves clarity of the equations.
The examiner respectfully disagrees. Based on the Replacement Sheet for FIG. 4, Equations 3 and 6 still include variables in the equations that are small, blurry, and difficult to read. While the Replacement Sheet is to provide clarity to the equations, there are still variables in the equations that are difficult to read. The objection to the drawing is therefore maintained.
With regards to claims 1, 5, 6 and the rejections under 35 U.S.C. 103, The applicant argues that Zheng does not teach “adaptively generating a data-dependent filter” because the filter bang in Zhang has 10 linearly spaced channels and a 50% overlap between adjacent filters, and the filter bank Zheng uses is a fixed, static 10 channel linear filter bank, and the same filter is applied for all drivers and all signals.
The examiner respectfully disagrees. The filtering performed using the filter bank, along with a prefiltering step, is computed in comparison to a threshold for calculating steering angle distance change. Page 17, left column, lines 1 – 26 discloses obtaining raw vehicle data, and filtering the data based on the amount of steering angle and additional maneuvers unrelated to lane changing and lane keeping events, with filtering based on the non-lane changing/keeping events also discussed on page 19, left column, lines 31 – 35 and page 20, left column, lines 12 – 16. The filtering performed is data-dependent, and interpreted as adaptively filtering based on the raw vehicle data obtained, which is not going to be the same type of data from vehicle to vehicle.
In addition, the applicants argue that Zheng does not appear to teach “travel data indicative of a real environment scene, because the teaching in Zheng is directed towards data relevant to the vehicle itself.
The examiner respectfully disagrees. Page 17, left column, lines 1 – 10 and page 20, left column, lines 12 - 16discloses obtaining raw vehicle data before filtering is performed, with the vehicle data includes maneuvers regarding backing out of a parking spot. In addition, FIG. 2 shows a test route map and driving scene on a residential and business road, with the figure showing an actual road with vehicles, a plurality of lanes, and a stop light.
With regards to claims 2 - 5 and the rejections under 35 U.S.C. 103, the applicant argues that since the dependent claims are dependent of claim 1 and views the claim allowable, dependent claims are allowable for at least the reasons discussed regarding the features of claim 1.
The examiner respectfully disagrees. As shown above, the prior art of Hartnett and Zheng discloses the features regarding claim 1, discussed above and shown in the rejections below. Therefore, the features the combination of Hartnett and Zheng disclosed in the previous Office Action are maintained in the current Office Action.
Drawings Objection
The drawings are objected to because the drawing in FIG. 4, filed on September 28, 2022, includes equations 3 and 6 that include variables which are small, blurry and difficult to read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 - 6 are rejected under 35 U.S.C. 103 as being unpatentable over Hartnett et al (U.S. PG Pub 2022/0105959 A1), hereinafter “Hartnett”, and further in view of Zheng et al (“Lane-Change Detection from Steering Signal Using Spectral Segmentation and Learning-Based Classification”), hereinafter “Zheng”.
As per claim 1, Hartnett discloses:
a scenario generation device comprising at least one processor (Hartnett, par [0003] discloses a system regarding an autonomous vehicle and an environment.)
at least one non-transitory computer readable storage medium storing a program, when executed by the at least one processor, causing the at least one processor to (Hartnett, par [0003] discloses a non-transitory computer readable medium with instructions for execution by the processor.)
obtaining unit obtaining a real environment scene, which is a scene that occurs in a real environment (Hartnett, par [0025] discloses obtaining data of an environment a vehicle is traveling, including environmental conditions, location of objects in the environment, and images of the environment, with par [0028] adds map data being created or retrieved that includes environment information surrounding the vehicle.)
from a travel database that stores travel data of a real vehicle (Hartnett, par [0043] discloses a map database used to provide data for retrieval to use with the autonomous vehicle, interpreted as including the map data.)
Hartnett does not expressly disclose:
adaptively generating a data-driven filter based on a frequency analysis result of analysis target data including the travel data indicative of the real environment scene; and
determining an evaluation scenario by filtering candidate scenarios using the filter, the candidate scenarios being generated based on a mathematical model.
Zheng however discloses:
adaptively generating a data-driven filter based on a frequency analysis result of analysis target data including the travel data indicative of the real environment scene (Zheng, page 17, left column, lines 1 - 26 discloses raw vehicle data obtained and filtered regarding the amount of steering angle and maneuvers unrelated to lane changing/keeping events, with additional info provided on page 19, left col, ln 31 - 35 and page 20, left col, ln 12 - 16, interpreted from the teaching that the data is dependent on the type of maneuvering the vehicle is performing, and the data kept vs the data filtered out, along with page 17, right column, lines 28 - 30 discloses a time-frequency spectral analysis method, with lines 38 -46 discloses using a filter bank based on a previous process to determine maneuver boundaries according to variations in spectral energy in individual frequency bands, with an average energy of the lower frequency bands computed as an output. FIG. 5 discloses the spectral analysis.)
including the travel data indicative of the real environment scene (Zheng, page 17, left column, lines 1 - 10 and page 20, left column, lines 12 - 16 discloses raw vehicle data obtained before filtering is performed, with vehicle data includes maneuvers a vehicle performs, including backing out of a parking spot. FIG. 2 on page 16 shows a test route map and an actual driving scene on a residential and business road, providing a visual of the real road with vehicles, a plurality of lanes, and a stop light.)
determining an evaluation scenario by filtering candidate scenarios using the filter (Zheng, page 16, right column, lines 5 - 10 discloses pre-processing performed to filtering out events deemed unnecessary and using the spectral analysis to obtain candidates regarding lane changes, with page 17, left column, lines 7 - 10, with reversal in parking spots as a type of event removed from the forward-moving scenarios.)
the candidate scenarios being generated based on a mathematical model (Zheng, page 20, left column, lines 12 - 17 discloses processing and filtering steps to remove noise and unnecessary events and generate non-uniform candidate events, with page 20, left column, lines 22 - 47 discloses the candidates and a mathematical equation used to determine a ratio regarding the lane-changing event length and time duration to provide an efficient average overall rate
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the vehicle traveling, travel map information and environment in which the vehicle travels teaching of Hartnett with the candidate events regarding forward-moving scenarios regarding vehicle teaching of Zheng. The motivation to do so would have been because Zheng discloses the benefit of using the proposed lane-change detection approach that can contribute to characterizing both automatic route recognition and distracted driving state analysis (Zheng, page 14, left column, Abstract, lines 23 - 25).
As per claim 5, Hartnett discloses:
a scenario generation method comprising the steps of obtaining a real environment scene, which is a scene that occurs in a real environment (Hartnett, par [0025] discloses obtaining data of an environment a vehicle is traveling, including environmental conditions, location of objects in the environment, and images of the environment, with par [0028] adds map data being created or retrieved that includes environment information surrounding the vehicle.)
from a travel database that stores travel data of a real vehicle (Hartnett, par [0043] discloses a map database used to provide data for retrieval to use with the autonomous vehicle, interpreted as including the map data.)
Hartnett does not expressly disclose:
adaptively generating a data-dependent filter based on a frequency analysis result of analysis target data including the travel data showing the real environment scene; and
determining an evaluation scenario by filtering candidate scenarios using the filter, the candidate scenarios being generated based on a mathematical model.
Zheng however discloses:
adaptively generating a data-dependent filter based on a frequency analysis result of analysis target data (Zheng, page 17, left column, lines 1 - 26 discloses raw vehicle data obtained and filtered regarding the amount of steering angle and maneuvers unrelated to lane changing/keeping events, with additional info provided on page 19, left col, ln 31 - 35 and page 20, left col, ln 12 - 16, interpreted from the teaching that the data is dependent on the type of maneuvering the vehicle is performing, and the data kept vs the data filtered out, along with page 17, right column, lines 28 - 30 discloses a time-frequency spectral analysis method, with lines 38 -46 discloses using a filter bank based on a previous process to determine maneuver boundaries according to variations in spectral energy in individual frequency bands, with an average energy of the lower frequency bands computed as an output. FIG. 5 discloses the spectral analysis.)
including the travel data indicative of the real environment scene (Zheng, page 17, left column, lines 1 - 10 and page 20, left column, lines 12 - 16 discloses raw vehicle data obtained before filtering is performed, with vehicle data includes maneuvers a vehicle performs, including backing out of a parking spot. FIG. 2 on page 16 shows a test route map and an actual driving scene on a residential and business road, providing a visual of the real road with vehicles, a plurality of lanes, and a stop light.)
determining an evaluation scenario by filtering candidate scenarios using the filter (Zheng, page 16, right column, lines 5 - 10 discloses pre-processing performed to filtering out events deemed unnecessary and using the spectral analysis to obtain candidates regarding lane changes, with page 17, left column, lines 7 - 10, with reversal in parking spots as a type of event removed from the forward-moving scenarios.)
the candidate scenarios being generated based on a mathematical model (Zheng, page 20, left column, lines 12 - 17 discloses processing and filtering steps to remove noise and unnecessary events and generate non-uniform candidate events, with page 20, left column, lines 22 - 47 discloses the candidates and a mathematical equation used to determine a ratio regarding the lane-changing event length and time duration to provide an efficient average overall rate.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the vehicle traveling, travel map information and environment in which the vehicle travels teaching of Hartnett with the candidate events regarding forward-moving scenarios regarding vehicle teaching of Zheng. The motivation to do so would have been because Zheng discloses the benefit of using the proposed lane-change detection approach that can contribute to characterizing both automatic route recognition and distracted driving state analysis (Zheng, page 14, left column, Abstract, lines 23 - 25).
As per claim 6, Hartnett discloses:
a scenario generation device comprising at least one processor (Hartnett, pars [0003] and [0075] discloses a system with components, including at least one type of processor.)
at least one non-transitory computer readable storage medium storing a program, when executed by the at least one processor, causing the at least one processor to (Hartnett, par [0003] discloses a non-transitory computer readable medium included with the processor in a system, with instructions for the processor to execute.)
obtain a real environment scene, which is a scene that occurs in a real environment (Hartnett, par [0025] discloses obtaining data of an environment a vehicle is traveling, including environmental conditions, location of objects in the environment, and images of the environment, with par [0028] adds map data being created or retrieved that includes environment information surrounding the vehicle.)
from a travel database that stores travel data of a real vehicle (Hartnett, par [0043] discloses a map database used to provide data for retrieval to use with the autonomous vehicle, interpreted as including the map data.)
Hartnett does not expressly disclose:
adaptively generate a data-dependent filter based on a frequency analysis result of analysis target data including the travel data indicative of the real environment scene; and
determine an evaluation scenario by filtering candidate scenarios using the generated filter, the candidate scenarios being generated based on a mathematical model.
Zheng however discloses:
adaptively generate a data-dependent filter based on a frequency analysis result of analysis target data (Zheng, page 17, left column, lines 1 - 26 discloses raw vehicle data obtained and filtered regarding the amount of steering angle and maneuvers unrelated to lane changing/keeping events, with additional info provided on page 19, left col, ln 31 - 35 and page 20, left col, ln 12 - 16, interpreted from the teaching that the data is dependent on the type of maneuvering the vehicle is performing, and the data kept vs the data filtered out, along with page 17, right column, lines 28 - 30 discloses a time-frequency spectral analysis method, with lines 38 -46 discloses using a filter bank based on a previous process to determine maneuver boundaries according to variations in spectral energy in individual frequency bands, with an average energy of the lower frequency bands computed as an output. FIG. 5 discloses the spectral analysis.)
including the travel data indicative of the real environment scene (Zheng, page 17, left column, lines 1 - 10 and page 20, left column, lines 12 - 16 discloses raw vehicle data obtained before filtering is performed, with vehicle data includes maneuvers a vehicle performs, including backing out of a parking spot. FIG. 2 on page 16 shows a test route map and an actual driving scene on a residential and business road, providing a visual of the real road with vehicles, a plurality of lanes, and a stop light.)
determine an evaluation scenario by filtering candidate scenarios using the generated filter (Zheng, page 16, right column, lines 5 - 10 discloses pre-processing performed to filtering out events deemed unnecessary and using the spectral analysis to obtain candidates regarding lane changes, with page 17, left column, lines 7 - 10, with reversal in parking spots as a type of event removed from the forward-moving scenarios.)
the candidate scenarios being generated based on a mathematical model (Zheng, page 20, left column, lines 12 - 17 discloses processing and filtering steps to remove noise and unnecessary events and generate non-uniform candidate events, with page 20, left column, lines 22 - 47 discloses the candidates and a mathematical equation used to determine a ratio regarding the lane-changing event length and time duration to provide an efficient average overall rate
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the vehicle traveling, travel map information and environment in which the vehicle travels teaching of Hartnett with the candidate events regarding forward-moving scenarios regarding vehicle teaching of Zheng. The motivation to do so would have been because Zheng discloses the benefit of using the proposed lane-change detection approach that can contribute to characterizing both automatic route recognition and distracted driving state analysis (Zheng, page 14, left column, Abstract, lines 23 - 25).
For claim 2: The combination of Hartnett and Zheng discloses claim 2: The scenario generation device according to claim 1, wherein
the candidate scenario is a scenario determined based on at least one of a cognitive performance, a traffic disturbance, and a vehicle motion performance that are represented by a mathematical model (Zheng, page 20, left column, lines 14 -19 and page 20, left column, lines 24 - 42 discloses candidates generated for potential lane changing events, with an evaluation performed including the use of an equation in use for the lane changing events.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the vehicle traveling, travel map information and environment in which the vehicle travels teaching of Hartnett with the candidate events regarding forward-moving scenarios regarding vehicle teaching of Zheng, and the additional teaching of lane changing event candidates, also found in Zheng. The motivation to do so would have been because Zheng discloses the benefit of using the proposed lane-change detection approach that can contribute to characterizing both automatic route recognition and distracted driving state analysis (Zheng, page 14, left column, Abstract, lines 23 - 25).
For claim 3: The combination of Hartnett and Zheng discloses claim 3: The scenario generation device according to claim 1, wherein the program further cause the at least one processor to
extract the real environment scene from the travel database by using an extraction logic defined for each of vehicle control functions (Hartnett, par [0043] discloses an environment that the autonomous vehicle travels, including intersections, objects (cyclists, motorcycles, different vehicles), traffic signs, as well as information of traffic and lane structures, obtained from a map database.)
For claim 4, Hartnett discloses:
the scenario generation device according to claim 1 further comprising wherein the program further cause the at least one processor to generate travel data indicative of a travel scene, the generator being learned by the real environment scene obtained by the real environment scene that is obtained with the travel data stored in the travel database as an input (Hartnett, par [0043] discloses a map database with map-based information from an environment, including roads and intersections in which an autonomous vehicle travels, objects in the environment including vehicles, motorcycles bicycles, and traffic information, with par [0050] adding classification of vehicles performed with the use of machine learning algorithms.)
Hartnett does not expressly disclose:
add the travel data that is generated to the analysis target data in addition to the travel data indicative of the real environment scene.
Zheng however discloses:
add the travel data that is generated to the analysis target data in addition to the travel data indicative of the real environment scene (Hartnett, page 16, right col, ln 4 - 7 discloses lane-changing detection using data and filtering out unnecessary data, including data related to a vehicle reversing, turning, and stopping, and using the data to perform analysis to determine lane0changing candidates.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the vehicle traveling, travel map information and environment in which the vehicle travels teaching of Hartnett with the candidate events regarding forward-moving scenarios regarding vehicle teaching of Zheng, and the additional teaching of using filtered data to determine the lane changing event candidates, also found in Zheng. The motivation to do so would have been because Zheng discloses the benefit of using the proposed lane-change detection approach that can contribute to characterizing both automatic route recognition and distracted driving state analysis (Zheng, page 14, left column, Abstract, lines 23 - 25).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CEDRIC D JOHNSON whose telephone number is (571)270-7089. The examiner can normally be reached M-Th 4:30am - 2:00pm, F 4:30am - 11:30am.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at 571-270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Cedric Johnson/ Primary Examiner, Art Unit 2186
June 6, 2026
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